This study examines the utility of semantically grounded labels in private music instruction, focusing on how they capture instructional intent and identify important teaching utterances. Our prior work introduced a framework for semantic analysis of classical guitar lessons, annotating teacher utterances with six Instructional Content Labels (ICL): Giving Subjective Information, Giving Objective Information, Asking Question, Giving Feedback, Giving Practice, and Giving Advice. In this study, we extend this framework by developing an ICL-weighted scoring method that combines utterance length with semantic weights to highlight instructionally significant discourse. We also reinterpret ICL categories for real-time spoken instruction, assigning higher weights to actionable guidance. To validate this approach, we compared our scoring outputs against rankings generated by multiple large language models—GPT-4.5, GPT-4o, and Claude Opus 4—across 24 classical guitar lessons. All models showed significantly stronger alignment with ICL-weighted scores than length-only baselines. Claude Opus 4 achieved near-perfect correlation (ρ = 0.993), while GPT-4.5 also demonstrated strong alignment (ρ = 0.902). These findings suggest that ICL-weighted scoring can capture instructional priorities and that general-purpose LLMs may approximate domain-specific judgments. The framework may provide a foundation for automated instructional analysis in music education.